llm-call

# LLM Call Replace `<skill_dir>` with the actual skill path shown by the loader. ## Procedure 1. Read the user's target model and prompt. 2. **Always pass `--model provider/model`**. If the user didn't name a specific model, pick a sensible default or run `--list` first to check available models. 3. Pass `--system`, `--max-tokens`, `--temperature`, `--stream`, or `--config` only when the task clearly requires them. 4. The script auto-detects config.yaml from the parent data dir hint when available, falling back to `{{DATA_DIR}}/config.yaml`. Use `--config` when calling a non-default profile explicitly. 5. Return the model output directly. If the call fails, summarize the provider or config error without inventing a fallback. ## Protocol mapping - `@ai-sdk/anthropic` -> `messages` - `@ai-sdk/openai` -> `chat/completions` - `@ai-sdk/google` -> `models/{model}:generateContent` ## Examples The script is a plain `.py` file — pick the Python launcher that exists on the host: | Platform | Launcher | |---|---| | macOS / Linux | `python3` (preferred) or `python` if it points at Python 3 | | Windows | `py -3` (preferred) or `python` | Example invocations (substitute the launcher above for `<py>`): ```bash <py> <skill_dir>/scripts/llm_call.py --model gemini/gemini-2.5-pro --system "Be brief" --prompt "Summarize this" <py> <skill_dir>/scripts/llm_call.py --model minimax-test/MiniMax-M3 --timeout 600 --stream --prompt "Long planning task" <py> <skill_dir>/scripts/llm_call.py --list ``` Do not assume `python3` exists on Windows — it is not part of a default install. Use `py -3` or the launcher resolved at runtime. ## Failure handling - If config.yaml is missing or incomplete, say which provider or credential is missing. - If the requested model is not configured, ask the user to choose from configured models. - If the HTTP request fails, surface the provider error; do not silently retry with another model.